PET Sinogram Analysis for Head Motion Detection
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Solution Overview
Problem
Head motion during long Positron Emission Tomography (PET) studies degrades image quality due to blurring and artifacts, leading to lower tumor detectability, inaccurate SUV calculation, and incorrect lesion or planning volumes in radiation therapy, and causes issues in attenuation correction of PET images.
Innovation Solution
A fully automated, data-driven approach that combines Principal Component Analysis (PCA) and correlation analysis to detect head motion from PET sinogram data, eliminating the need for external tracking systems by binning data into frames with minimal intra-frame motion and compensating for motion between stationary positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external tracking systems are used to monitor head motion during PET scans, then head motion detection accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The PET scanner uses its own acquired imaging data to detect head motion, eliminating the need for external tracking systems. The system performs self-monitoring by analyzing changes in the PET data itself to identify motion events during the scan.
Solution Approach 2:
The patent extracts motion information from the PET imaging data itself, separating the motion detection function from the imaging function. By analyzing temporal changes and statistical properties of the acquired data, the system isolates motion signals without requiring additional external devices.
2Manufacturing precision
If head motion is detected and corrected, then image quality is improved, but processing time and computational complexity increase
Solution Approach 1:
The system continuously monitors for head motion throughout the scan and identifies motion events in real-time. By detecting motion as it occurs and segmenting the data accordingly, the system prepares motion-corrected datasets without requiring extensive post-processing of the entire scan.
Solution Approach 2:
The patent divides the continuous PET scan data into multiple segments or frames based on detected motion events. Each segment is processed independently for motion correction, allowing efficient handling of large datasets by breaking them into manageable portions that can be corrected and recombined.
Data Source
AI summary
According to one embodiment, a method of image analysis is provided. The method includes binning image data into a plurality of sinogram frames, identifying a plurality of initial stationary frames by applying a first analysis technique on the plurality of binned sinogram frames, extracting a plurality of first statistical parameters applying a second analysis technique on the plurality of binned sinogram frames, combining the plurality of first statistical parameters with boundaries of plurality of initial stationary frames to generate a presentation of a joint analysis combining at least some of the plurality of the first statistical parameters and at least some of the plurality of the second statistical parameter, identifying a plurality of final stationary frames from the presentation of the joint analysis, independently reconstructing each of the plurality of final stationary frames, and registering each of the plurality of final stationary frames to a first state.